ANR PRIOSENS (2021/2025)

Mon, 27 Apr 2020 00:00:00 +0000

A fundamental goal of systems neuroscience is to describe how sensory inputs are integrated and guide an animal’s behavior. To be able to integrate these inputs, early sensory systems have developed selectivities for specific stimulus features that allow them to analyze the inputs using these features as basis. We aim to uncover how disparate motion signals are integrated to produce a global percept of motion, and to understand the conditions in which such integration fails. Our proposal reflects the fact that adaptive behaviors in complex environments face numerous challenges, from processing noisy and uncertain visual motion information to predict future events on target trajectory contingencies and its interactions with a dynamic, cluttered environment. We propose to use dynamic inference as an efficient theoretical framework to understand how the brain integrates Prior knowledges elaborated from statistical regularities of natural environments with different sources of information across different time scales in order to extract relevant motion information from the sensory flow and predict future events or actions. The smooth pursuit system is an excellent probe of such hierarchical dynamical inferences from target motion computation to target trajectory prediction. In marmosets, we have access to populations of neurons in pivotal cortical areas along the occipito-parieto- frontal network that have been identified in non-human and human primates. We seek to uncover a unifying empirical and theoretical framework to capture inference across different time scales.

With Guilhem Ibos, Guillaume Masson & Nicholas Priebe.

Aim 3, modelling behavioural and neuronal data within the active inference framework

Type de contrat : CRCNS US-French Research Proposal - ANR-CRCNS-2020
Durée: 4 ans, à partir du 1er novembre 2020 - prolongatio au 10/2025
Budget total (partenaire français): 341 k€
to be recruited: Post-doctoral fellow: A post-post-doctoral fellow in computational neuroscience will be recruited. With a 2-5 years experience, salary cost is of 52K€/year, for 2 years (total: 104K€).
Coordinateur Scientifique : MONTAGNINI, Anna & PERRINET Laurent (UMR7289)
Partenaire(s) : AGENCE NATIONALE DE LA RECHERCHE
Responsable Scientifique INT : MASSON Guillaume (UMR7289)

Acknowledgement

This work was supported by ANR project “PRIOSENS” N° ANR-20-NEUC-0002.

Jean-Bernard Damasse

Mon, 01 Oct 2018 00:00:00 +0000

Smooth pursuit eye movements and learning: Role of motion probability and reinforcement contingencies (PhD, 2014-2017)

Thesis director: Anna Montagnini
Thesis co-supervision: Laurent Perrinet

In the continuous flow of sensory evidence, cognitive systems must provide rapid behavioral choices across different time scales. For instance, seeing a moving object may result in various responses such as catching or avoiding collision depending on the trajectory and the nature of the object, but also depending on the recent experience and the expectations associated with that object and its motion properties. The principal goal of the larger scientific project in which this PhD thesis is inscribed (see ANR-REM project) is the analysis of reinforcement learning processes in the domain of voluntary eye movements (saccades and smooth pursuit eye movements) in humans. Within this PhD project we will use a dual approach, based on behavioural experiments on human subjects and on computational modelling of the experimental data, in order to address this important question, with a particular emphasis on the time course of learning effects and on the hypothesised role of probabilistic inference as underlying mechanism. «BR» Visually driven eye movements provide an ideal experimental preparation to probe sensorimotor behavior across different time-scales, processing levels (from sensory encoding to the final categorical choice) and movement repertoire (e.g. smooth pursuit and saccades). In addition, a remarkable flexibility of oculomotor behaviors has been highlighted by manipulating the expectancy for sensory features or the outcome associated to particular motor responses.

thesis available @ https://www.theses.fr/s137225

Behavioral Neuroscience | Laurent Perrinet

ANR PRIOSENS (2021/2025)

Aim 3, modelling behavioural and neuronal data within the active inference framework

Acknowledgement

Jean-Bernard Damasse

Smooth pursuit eye movements and learning: Role of motion probability and reinforcement contingencies (PhD, 2014-2017)